Overview

Dataset statistics

Number of variables15
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.7 KiB
Average record size in memory64.3 B

Variable types

Categorical9
Numeric6

Alerts

feat.e is highly correlated with feat.iHigh correlation
feat.f is highly correlated with responseHigh correlation
feat.i is highly correlated with feat.eHigh correlation
response is highly correlated with feat.fHigh correlation
feat.g_x is highly correlated with feat.g_y and 1 other fieldsHigh correlation
feat.g_y is highly correlated with feat.g_x and 1 other fieldsHigh correlation
feat.g_z is highly correlated with feat.g_x and 1 other fieldsHigh correlation
feat.c_a is highly correlated with feat.c_bHigh correlation
feat.c_b is highly correlated with feat.c_a and 1 other fieldsHigh correlation
feat.c_d is highly correlated with feat.c_bHigh correlation
feat.a has unique values Unique
feat.e has unique values Unique
feat.f has unique values Unique
feat.h has unique values Unique
feat.i has unique values Unique

Reproduction

Analysis started2022-11-22 19:51:19.627091
Analysis finished2022-11-22 19:51:24.344589
Duration4.72 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

response
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
553 
0
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1553
55.3%
0447
44.7%

Length

2022-11-22T14:51:24.389091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:24.451091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1553
55.3%
0447
44.7%

Most occurring characters

ValueCountFrequency (%)
1553
55.3%
0447
44.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1553
55.3%
0447
44.7%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1553
55.3%
0447
44.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1553
55.3%
0447
44.7%

feat.a
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.048383598
Minimum-7.429324037
Maximum10.7231198
Zeros0
Zeros (%)0.0%
Negative353
Negative (%)35.3%
Memory size7.9 KiB
2022-11-22T14:51:24.524590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.429324037
5-th percentile-3.867752929
Q1-0.8849727281
median1.027628916
Q32.9938056
95-th percentile6.028401614
Maximum10.7231198
Range18.15244384
Interquartile range (IQR)3.878778328

Descriptive statistics

Standard deviation2.975084929
Coefficient of variation (CV)2.837782788
Kurtosis-0.0686019667
Mean1.048383598
Median Absolute Deviation (MAD)1.95091297
Skewness0.06539204332
Sum1048.383598
Variance8.851130336
MonotonicityNot monotonic
2022-11-22T14:51:24.611091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.68142693971
 
0.1%
0.11771401851
 
0.1%
3.3071568851
 
0.1%
1.3621579881
 
0.1%
3.5909453021
 
0.1%
5.1415435851
 
0.1%
6.8987440461
 
0.1%
0.91481483581
 
0.1%
-5.7471532711
 
0.1%
1.0945780141
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-7.4293240371
0.1%
-6.9827683951
0.1%
-6.9294468561
0.1%
-6.8050990111
0.1%
-6.5237534071
0.1%
-6.3976945811
0.1%
-5.9275066271
0.1%
-5.7471532711
0.1%
-5.6749630891
0.1%
-5.6318993321
0.1%
ValueCountFrequency (%)
10.72311981
0.1%
9.075142011
0.1%
9.0545769981
0.1%
8.7263492911
0.1%
8.7143744381
0.1%
8.659078341
0.1%
8.4639936321
0.1%
8.3741814761
0.1%
8.2906799571
0.1%
8.2503200611
0.1%

feat.b
Real number (ℝ)

Distinct993
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.941324755
Minimum-8.571791335
Maximum1.085556232
Zeros0
Zeros (%)0.0%
Negative995
Negative (%)99.5%
Memory size7.9 KiB
2022-11-22T14:51:24.693589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-8.571791335
5-th percentile-6.482529863
Q1-4.978745311
median-3.917721434
Q3-2.893249761
95-th percentile-1.60183933
Maximum1.085556232
Range9.657347567
Interquartile range (IQR)2.08549555

Descriptive statistics

Standard deviation1.506601557
Coefficient of variation (CV)-0.3822576547
Kurtosis-0.06023898819
Mean-3.941324755
Median Absolute Deviation (MAD)1.048129977
Skewness-0.0231639689
Sum-3941.324755
Variance2.269848252
MonotonicityNot monotonic
2022-11-22T14:51:24.774089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.9177214348
 
0.8%
-5.4936980871
 
0.1%
-2.4811942091
 
0.1%
-2.6678894491
 
0.1%
-6.9099102321
 
0.1%
-2.4651973671
 
0.1%
-3.991813411
 
0.1%
-3.1453315461
 
0.1%
-6.4798833451
 
0.1%
-4.999981571
 
0.1%
Other values (983)983
98.3%
ValueCountFrequency (%)
-8.5717913351
0.1%
-8.0429940541
0.1%
-7.9439881621
0.1%
-7.9060572561
0.1%
-7.8240141621
0.1%
-7.6938625251
0.1%
-7.5661103881
0.1%
-7.5039209981
0.1%
-7.4706036611
0.1%
-7.3765677631
0.1%
ValueCountFrequency (%)
1.0855562321
0.1%
0.9357761651
0.1%
0.77606671111
0.1%
0.2241264141
0.1%
0.19608672041
0.1%
-0.13409785571
0.1%
-0.2818812981
0.1%
-0.32800298951
0.1%
-0.36326628831
0.1%
-0.37568894031
0.1%

feat.d
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1.0
520 
0.0
480 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0520
52.0%
0.0480
48.0%

Length

2022-11-22T14:51:24.844590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:24.912089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0520
52.0%
0.0480
48.0%

Most occurring characters

ValueCountFrequency (%)
01480
49.3%
.1000
33.3%
1520
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
66.7%
Other Punctuation1000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01480
74.0%
1520
 
26.0%
Other Punctuation
ValueCountFrequency (%)
.1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01480
49.3%
.1000
33.3%
1520
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01480
49.3%
.1000
33.3%
1520
 
17.3%

feat.e
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5183207529
Minimum-6.758176383
Maximum5.289708782
Zeros0
Zeros (%)0.0%
Negative596
Negative (%)59.6%
Memory size7.9 KiB
2022-11-22T14:51:24.973090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6.758176383
5-th percentile-3.840913833
Q1-1.779296264
median-0.5163815105
Q30.8010775699
95-th percentile2.774485945
Maximum5.289708782
Range12.04788516
Interquartile range (IQR)2.580373833

Descriptive statistics

Standard deviation1.984703368
Coefficient of variation (CV)-3.829102649
Kurtosis-0.03436963598
Mean-0.5183207529
Median Absolute Deviation (MAD)1.285863659
Skewness-0.07150994541
Sum-518.3207529
Variance3.939047458
MonotonicityNot monotonic
2022-11-22T14:51:25.056590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.80061495571
 
0.1%
-0.41054292741
 
0.1%
-2.1491909771
 
0.1%
0.66916116741
 
0.1%
-2.4965973321
 
0.1%
-3.4685630121
 
0.1%
0.015554966171
 
0.1%
0.33058000811
 
0.1%
1.5508391461
 
0.1%
0.95215213581
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-6.7581763831
0.1%
-6.3997435691
0.1%
-6.3359524281
0.1%
-6.1787523341
0.1%
-5.8563288221
0.1%
-5.8193387591
0.1%
-5.7190500181
0.1%
-5.4730478091
0.1%
-5.2715855021
0.1%
-5.1665747551
0.1%
ValueCountFrequency (%)
5.2897087821
0.1%
4.8074814571
0.1%
4.6989834111
0.1%
4.6969804641
0.1%
4.6288186011
0.1%
4.5807372481
0.1%
4.4662102251
0.1%
4.2456769571
0.1%
3.9712055371
0.1%
3.963994221
0.1%

feat.f
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.257345469
Minimum-31.09907622
Maximum21.56793583
Zeros0
Zeros (%)0.0%
Negative789
Negative (%)78.9%
Memory size7.9 KiB
2022-11-22T14:51:25.139589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-31.09907622
5-th percentile-19.48796709
Q1-11.65484815
median-6.262428864
Q3-0.912532982
95-th percentile6.307771472
Maximum21.56793583
Range52.66701205
Interquartile range (IQR)10.74231517

Descriptive statistics

Standard deviation8.005529545
Coefficient of variation (CV)-1.279381103
Kurtosis0.1736241289
Mean-6.257345469
Median Absolute Deviation (MAD)5.371094591
Skewness0.02786552246
Sum-6257.345469
Variance64.0885033
MonotonicityNot monotonic
2022-11-22T14:51:25.222590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.4276017881
 
0.1%
5.7338683131
 
0.1%
-16.235341371
 
0.1%
-4.5279111151
 
0.1%
-11.992206131
 
0.1%
-10.865188251
 
0.1%
-2.6410910021
 
0.1%
0.73479836511
 
0.1%
-2.9587445061
 
0.1%
-10.278754641
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-31.099076221
0.1%
-30.345078741
0.1%
-29.101038631
0.1%
-28.744143161
0.1%
-28.028870031
0.1%
-25.933495851
0.1%
-25.908219451
0.1%
-25.611928081
0.1%
-25.588970831
0.1%
-25.035811091
0.1%
ValueCountFrequency (%)
21.567935831
0.1%
20.174262011
0.1%
19.884342531
0.1%
17.932202621
0.1%
17.772680271
0.1%
17.39059161
0.1%
14.179184561
0.1%
14.014120771
0.1%
13.320451141
0.1%
13.043309091
0.1%

feat.h
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.03083308
Minimum3.421248303
Maximum17.43144145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-11-22T14:51:25.304590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.421248303
5-th percentile6.692429352
Q18.700144228
median10.02830899
Q311.52875894
95-th percentile13.25505024
Maximum17.43144145
Range14.01019315
Interquartile range (IQR)2.828614715

Descriptive statistics

Standard deviation2.022200156
Coefficient of variation (CV)0.2015984257
Kurtosis0.03984029824
Mean10.03083308
Median Absolute Deviation (MAD)1.421113657
Skewness-0.1302689443
Sum10030.83308
Variance4.089293472
MonotonicityNot monotonic
2022-11-22T14:51:25.382589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.254198871
 
0.1%
6.7543034271
 
0.1%
11.242786191
 
0.1%
12.682988481
 
0.1%
9.3523765561
 
0.1%
10.288766941
 
0.1%
7.8362944321
 
0.1%
9.9880453161
 
0.1%
9.2779886321
 
0.1%
8.4933109241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
3.4212483031
0.1%
3.4757013311
0.1%
3.9459085621
0.1%
4.1514177361
0.1%
4.227701751
0.1%
4.516772241
0.1%
4.6143975021
0.1%
4.862270611
0.1%
5.0221927721
0.1%
5.2175520221
0.1%
ValueCountFrequency (%)
17.431441451
0.1%
16.167479081
0.1%
15.857801481
0.1%
15.697488071
0.1%
14.834141221
0.1%
14.790402651
0.1%
14.623962921
0.1%
14.623638891
0.1%
14.488327261
0.1%
14.476326451
0.1%

feat.i
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5186066973
Minimum-6.763426764
Maximum5.315728559
Zeros0
Zeros (%)0.0%
Negative598
Negative (%)59.8%
Memory size7.9 KiB
2022-11-22T14:51:25.598589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6.763426764
5-th percentile-3.886815871
Q1-1.773089044
median-0.5060849539
Q30.8030406826
95-th percentile2.78705719
Maximum5.315728559
Range12.07915532
Interquartile range (IQR)2.576129727

Descriptive statistics

Standard deviation1.984378137
Coefficient of variation (CV)-3.826364271
Kurtosis-0.02932702066
Mean-0.5186066973
Median Absolute Deviation (MAD)1.292620753
Skewness-0.07130431438
Sum-518.6066973
Variance3.937756592
MonotonicityNot monotonic
2022-11-22T14:51:25.679590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.82807286971
 
0.1%
-0.33587886621
 
0.1%
-2.1387549691
 
0.1%
0.75678816971
 
0.1%
-2.5055393611
 
0.1%
-3.4908921391
 
0.1%
0.070549327411
 
0.1%
0.36395920211
 
0.1%
1.6075694571
 
0.1%
0.97584825671
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-6.7634267641
0.1%
-6.3987461961
0.1%
-6.3762774091
0.1%
-6.1923099231
0.1%
-5.8456334141
0.1%
-5.829952191
0.1%
-5.7561982921
0.1%
-5.5224548281
0.1%
-5.2151543721
0.1%
-5.1740469741
0.1%
ValueCountFrequency (%)
5.3157285591
0.1%
4.8429657361
0.1%
4.7159034841
0.1%
4.6462573291
0.1%
4.5883745311
0.1%
4.5500446821
0.1%
4.4465088741
0.1%
4.2480040111
0.1%
3.9661866681
0.1%
3.9470042531
0.1%

feat.c_a
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
760 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

Length

2022-11-22T14:51:25.750089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:25.806589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0760
76.0%
1240
 
24.0%

feat.c_b
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
722 
1
278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

Length

2022-11-22T14:51:25.857592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:25.915092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0722
72.2%
1278
 
27.8%

feat.c_c
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
773 
1
227 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

Length

2022-11-22T14:51:25.968092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:26.031089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0773
77.3%
1227
 
22.7%

feat.c_d
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
745 
1
255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

Length

2022-11-22T14:51:26.084089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:26.148089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0745
74.5%
1255
 
25.5%

feat.g_x
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
670 
1
330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0670
67.0%
1330
33.0%

Length

2022-11-22T14:51:26.204089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:26.259589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0670
67.0%
1330
33.0%

Most occurring characters

ValueCountFrequency (%)
0670
67.0%
1330
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0670
67.0%
1330
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0670
67.0%
1330
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0670
67.0%
1330
33.0%

feat.g_y
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
671 
1
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0671
67.1%
1329
32.9%

Length

2022-11-22T14:51:26.311590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:26.380589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0671
67.1%
1329
32.9%

Most occurring characters

ValueCountFrequency (%)
0671
67.1%
1329
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0671
67.1%
1329
32.9%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0671
67.1%
1329
32.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0671
67.1%
1329
32.9%

feat.g_z
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
659 
1
341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Length

2022-11-22T14:51:26.434089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T14:51:26.491089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Most occurring characters

ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0659
65.9%
1341
34.1%

Interactions

2022-11-22T14:51:23.587588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:20.615588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.413089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.923589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.462593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.977589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.669588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:20.985091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.499089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.015589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.553089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.061590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.742090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.063588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.581089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.095588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.638588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.159589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.819091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.148592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.670089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.175590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.725088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.246590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.900089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.228588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.752589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.257590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.808088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.419591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.978091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.320591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:21.834090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.350589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:22.894088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-22T14:51:23.502088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-22T14:51:26.550591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-22T14:51:26.675591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-22T14:51:26.793590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-22T14:51:26.906090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-22T14:51:27.024091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-22T14:51:27.123089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-22T14:51:24.123090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-22T14:51:24.288089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

responsefeat.afeat.bfeat.dfeat.efeat.ffeat.hfeat.ifeat.c_afeat.c_bfeat.c_cfeat.c_dfeat.g_xfeat.g_yfeat.g_z
01-0.681427-5.4936980.0-0.800615-4.42760210.254199-0.8280730100001
110.309468-5.5599331.0-1.155514-0.7990949.084749-1.1096980001100
215.676125-4.0269701.0-3.396331-0.6319668.753848-3.4174170100010
311.211525-4.1982631.0-1.894569-16.27326212.191295-1.9048011000010
411.387863-7.8240141.04.696980-22.2088779.6266864.7159030010001
516.145195-2.4391400.0-0.57483011.64260912.362962-0.5214230010010
612.382749-3.6254110.01.326984-4.1488819.2261221.2876181000001
71-2.795184-0.3756891.0-0.869053-2.9948627.973038-0.8393260010100
80-1.060559-2.9722030.00.719649-15.54374812.8931240.7185030100001
91-0.336986-4.6704391.0-0.6054543.0603999.803020-0.5486100100010

Last rows

responsefeat.afeat.bfeat.dfeat.efeat.ffeat.hfeat.ifeat.c_afeat.c_bfeat.c_cfeat.c_dfeat.g_xfeat.g_yfeat.g_z
99013.027287-5.6457091.0-4.847993-8.07024612.043355-4.8942860100100
9911-2.222620-2.6117330.00.735233-2.87674110.0907260.8165451000100
99202.363733-3.6298010.0-5.109591-7.5781629.301541-5.1199360100010
99300.360079-5.1051570.0-1.393937-21.57559610.537327-1.3978650001010
99401.939686-5.9200130.00.098981-17.42110511.7055790.0848550100001
99500.730074-3.8850350.0-3.356949-12.80334411.204110-3.3966730100001
99614.211548-3.6172530.02.0349956.9957539.2080892.0697521000001
9971-3.053301-3.5838301.01.929012-7.0131057.6378621.8563560010001
9981-0.567850-3.1947161.0-1.8497124.20481611.725868-1.8624660010001
99910.252428-4.6907281.01.742044-4.5640317.9097091.7470370001010